Refining Channel Estimation in IRS-Assisted Communication via Diffusion Model

The paper proposes a diffusion-based channel estimation refinement (DB-CER) approach to improve the accuracy of cascaded channel estimates in intelligent reflecting surface (IRS) governed communication systems. The DB-CER method addresses the problem of non-linear channel responses in cascaded channels, which can lead to inaccurate estimates using the traditional least squares (LS) method. By utilizing the non-linear processing capability of the diffusion model, the proposed method provides a more accurate estimate of the cascaded channel response, while also being able to handle complex noise patterns that are often present in practical communication scenarios. The method does not make any assumptions about the channel response, making it a more general solution that can be applied in various communication scenarios. Experiments show that the proposed DB-CER method outperforms both the LS method and state-of-the-art deep learning-based denoising methods in terms of normalized mean square error (NMSE).